Why the study?
Cardiovascular autonomic modulation and cardiorespiratory fitness are well-established predictors of health, but metabolic signatures representing their integrated profiles in apparently healthy individuals needed investigation.
Specific serum metabolites demonstrate sex-dependent associations with integrated profiles of cardiovascular autonomic modulation and cardiorespiratory fitness in apparently healthy individuals.
Should not change current practice; leaves open sex-specific metabolites as modulators of autonomic function and cardiorespiratory fitness.
Cardiovascular autonomic modulation (CAM) and cardiorespiratory fitness (CRF) are well-established predictors of health. Identifying metabolites associated with integrated CAM-CRF profiles may help characterize healthy physiological states. This study aimed to investigate metabolic signatures representing distinct CAM-CRF profiles in apparently healthy individuals. Non-obese individuals (n = 127, 43 ± 14 years) underwent fasting blood collection for serum metabolome (SM) analysis, cardiovascular assessment, and a cardiopulmonary exercise test to access CAM and CRF. CAM-CRF profiles were obtained separately by sex using principal components analysis (PCA) of CAM and CRF. Subjects' scores from the first two principal components of the PCA were used to generate the groups. Groups' SM were compared using one-way ANOVA (controlling for age) and metabolite correlations were analyzed using the subjects' scores (controlling for age and body mass index), considering p < 0.01. In females, low sebacic acid levels were associated with high cardiac parasympathetic modulation (CPM) and greater cardiovascular complexity. In males, low ornithine levels corresponded to a profile with high CPM, baroreflex sensitivity (BRS), and CRF. Choline, betaine, N,N-dimethylglycine levels in females, and glucose and sarcosine in males, were negatively correlated with CPM, BRS, CRF and cardiovascular complexity. These metabolites reflect integrated CAM-CRF conditions, enhancing the understanding of underlying metabolic profiles.
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Signini et al. (2026) studied this question.
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